In accordance with embodiments, there are provided mechanisms and methods for selecting a synchronous or asynchronous process to determine a forecast. These mechanisms and methods for such synchronous/asynchronous process selection can enable embodiments to determine forecasts for multiple users (e.g. with hierarchical relationships, etc.) over an arbitrary time interval. The ability of embodiments to provide forecasts that involve such a large amount of data in an effective way can enable forecasting that was otherwise infeasible due to resource limitations.
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1. A method, comprising: identifying at least one user associated with at least one deal for which a sales forecast is determined; identifying data upon which the sales forecast is based; identifying a type of event that affects the sales forecast; managing load on a forecast system utilized to determine the sales forecast, by automatically selecting between a synchronous process and an asynchronous process for determining the sales forecast, by a processor, based on the identified type of event, including: automatically selecting the synchronous process when the type of event affects the data upon which the sales forecast is based; and automatically selecting the asynchronous process when the type of event does not affect the data upon which the sales forecast is based, wherein the type of event that does not affect the data is based at least on changes to a defined reporting structure for the determined sales forecast; determining the sales forecast from the data, utilizing the selected one of the synchronous process and the asynchronous process; wherein the synchronous process is performed in less time than the asynchronous process, and wherein the synchronous process is performed in real-time or near-real time and the asynchronous process includes batch processing that is performed in a greater amount of time with respect to the synchronous process, such that the synchronous process is selected to determine the sales forecast faster than would the asynchronous process.
A sales forecasting system automatically chooses between a fast (synchronous) or slower (asynchronous) process to generate sales forecasts for users and their deals. The system identifies users, deal data used for forecasting, and event types affecting forecasts. A processor manages system load by selecting the process. If an event *changes the deal data* (e.g., modifying a deal size), the fast, real-time synchronous process is selected. If an event *doesn't change the deal data* but changes the reporting structure or metadata, the slower, batch-oriented asynchronous process is chosen. This ensures forecasts are updated quickly when data changes, while offloading less critical reporting updates to a background process.
2. The method of claim 1 , wherein the data is associated with the at least one user.
The sales forecasting system described above, which automatically selects between synchronous and asynchronous forecasting processes based on event type, includes a deal dataset that is directly associated with a specific user. This means the system tracks which user's deals are being used in the sales forecast calculations, allowing for forecasts to be tied directly to individual users or sales representatives.
3. The method of claim 1 , wherein the type of the event affecting the data upon which the sales forecast is based includes a modification to the data upon which the sales forecast is based.
In the sales forecasting system that automatically chooses between forecast processes, the type of event that triggers the selection of the synchronous (fast) process is a modification to the data used to build the sales forecast. This means if something *directly changes the numbers* going into the forecast (like deal size, probability, or close date), the system will prioritize updating the forecast quickly via the synchronous process.
4. The method of claim 3 , wherein the synchronous process is automatically selected when the type of the event includes the modification to the data upon which the sales forecast is based such that the determining of the sales forecast from the data utilizing the selected synchronous process adjusts the sales forecast based on current data resulting from the modification to the data.
Building upon the description of the forecasting system, when a data modification event occurs, the system chooses the synchronous process to immediately recalculate the sales forecast based on the new, current data. This ensures that any changes to the underlying data are reflected in the sales forecast as quickly as possible, providing up-to-date information.
5. The method of claim 1 , wherein the type of event that does not affect the data upon which the sales forecast is based is a set-up function event which changes metadata including parameters defining the reporting structure for reporting the determined sales forecast.
In the forecasting system, if a change does not affect the sales data, the system utilizes an asynchronous process. This specifically applies to metadata or setup functions that redefine the reporting structure or parameters without altering the data feeding the sales forecast. For example, changing which columns appear on a report or updating the names of sales territories would trigger the asynchronous process.
6. The method of claim 1 , further comprising identifying a hierarchy associated with the at least one user.
The described sales forecasting system also identifies and considers a hierarchy related to the users. This means that the system understands the relationships between users, such as manager/subordinate, team structures, or other organizational hierarchies that might be relevant to forecasting.
7. The method of claim 6 , wherein the sales forecast is determined, based on the hierarchy.
The sales forecasting system uses the identified user hierarchy to generate sales forecasts. This means that the system can aggregate or disaggregate forecasts based on the hierarchical structure, allowing for roll-up forecasts from individual contributors to team totals, or forecasts broken down by management levels.
8. The method of claim 6 , wherein the hierarchy is user-defined.
The hierarchy used in the sales forecasting system is user-defined. This means the system allows administrators or users to configure the specific structure of the hierarchy, enabling the system to adapt to different organizational structures and reporting needs, rather than being limited to a fixed or pre-defined hierarchy.
9. The method of claim 1 , further comprising identifying at least one additional user associated with the at least one user.
The sales forecasting system recognizes and associates multiple users, in addition to the primary user for whom the forecast is being generated. This implies the system can relate forecasts for multiple users and consider their impact on each other's sales forecasts or on the overall forecast.
10. The method of claim 9 , wherein the sales forecast is determined, based on the at least one additional user.
The sales forecasting system determines the sales forecast based on data from additional users related to the primary user. This means forecasts aren't isolated to a single user's activity, but can incorporate the influence or impact of related users, reflecting team selling or hierarchical relationships.
11. The method of claim 10 , wherein each of a plurality of additional users are included in a list associated with the user.
The forecasting system includes multiple related users by organizing them into a user list associated with the primary user. This allows for easy management and grouping of related users, and ensures that their data is included in the forecast calculations.
12. The method of claim 11 , wherein the list is user-defined.
The list of related users in the forecasting system is user-defined. This gives users control over who is considered a related user, allowing for customized and relevant forecasting based on specific team structures or reporting relationships.
13. The method of claim 10 , wherein the determination is made by identifying a first sales forecast associated with the at least one user and a second sales forecast associated with the at least one additional user, and aggregating the first sales forecast and the second sales forecast.
The system forecasts by combining individual forecasts. It identifies a first sales forecast for a main user and a second sales forecast for at least one other user, then adds those forecasts together to create a combined forecast.
14. The method of claim 1 , further comprising identifying an arbitrary time period.
The sales forecasting system allows for the definition of an arbitrary time period for the forecast. This means the system isn't restricted to pre-set intervals like monthly or quarterly, but can handle custom timeframes for forecasting, such as project-specific durations or irregular reporting cycles.
15. The method of claim 14 , wherein the sales forecast is determined, based on the arbitrary time period.
The sales forecast is determined based on the arbitrary time period, meaning the system calculates and presents the forecast specifically for the chosen timeframe, whether it's a standard month, a custom quarter, or a specific project duration.
16. The method of claim 1 , further comprising identifying the at least one event, wherein the sales forecast is determined based on the at least one event.
The sales forecasting system identifies at least one event and generates a sales forecast based on that event. This suggests the system can trigger forecast calculations based on specific occurrences, such as a large deal closing, a marketing campaign launch, or an economic shift.
17. The method of claim 1 , wherein predetermined criteria is utilized for the automatic selection between the synchronous process and the asynchronous process, such that first predetermined criteria based upon which the synchronous process is automatically selected includes a type of event occurring and being identified that affects the data upon which the sales forecast is based, and second predetermined criteria based upon which the asynchronous process is automatically selected includes a type of event occurring and being identified that does not affect the data upon which the sales forecast is based.
The sales forecasting system automatically selects between the synchronous and asynchronous processes using pre-defined criteria. The system selects the synchronous process when the event directly modifies the underlying sales data. Conversely, the asynchronous process is automatically chosen when the event involves changes to metadata, such as modifications to the reporting structure.
18. A machine-readable medium carrying one or more sequences of instructions which, when executed by one or more processors, cause the one or more processors to carry out the steps of: identifying at least one user associated with at least one deal for which a sales forecast is determined; identifying data upon which the sales forecast is based; identifying a type of event that affects the sales forecast; managing load on a forecast system utilized to determine the sales forecast, by automatically selecting between a synchronous process and an asynchronous process for determining the sales forecast, by a processor, based on the identified type of event, including: automatically selecting the synchronous process when the type of event affects the data upon which the sales forecast is based; and automatically selecting the asynchronous process when the type of event does not affect the data upon which the sales forecast is based, wherein the type of event that does not affect the data is based at least on changes to a defined reporting structure for the determined sales forecast; determining the sales forecast from the data, utilizing the selected one of the synchronous process and the asynchronous process; wherein the synchronous process is performed in less time than the asynchronous process, and wherein the synchronous process is performed in real-time or near-real time and the asynchronous process includes batch processing that is performed in a greater amount of time with respect to the synchronous process, such that the synchronous process is selected to determine the sales forecast faster than would the asynchronous process.
A computer-readable storage medium contains instructions that, when executed, cause a computer to perform these steps: identify a user and their deals needing a sales forecast; identify the data used for the forecast; identify the event type that affects the forecast. The system then manages its load by choosing either a fast (synchronous) or slow (asynchronous) forecast process based on the event type. If the event *changes deal data*, the fast process is used. If the event *changes reporting structure*, the slow process is used. Finally, the system generates the forecast using the chosen process.
19. An apparatus, comprising: a processor; and one or more stored sequences of instructions which, when executed by the processor, cause the processor to carry out the steps of: identifying at least one user associated with at least one deal for which a sales forecast is determined; identifying data upon which the sales forecast is based; identifying a type of event that affects the sales forecast; managing load on a forecast system utilized to determine the sales forecast, by automatically selecting between a synchronous process and an asynchronous process for determining the sales forecast, by a processor, based on the identified type of event, including: automatically selecting the synchronous process when the type of event affects the data upon which the sales forecast is based; and automatically selecting the asynchronous process when the type of event does not affect the data upon which the sales forecast is based, wherein the type of event that does not affect the data is based at least on changes to a defined reporting structure for the determined sales forecast; determining the sales forecast from the data, utilizing the selected one of the synchronous process and the asynchronous process; wherein the synchronous process is performed in less time than the asynchronous process, and wherein the synchronous process is performed in real-time or near-real time and the asynchronous process includes batch processing that is performed in a greater amount of time with respect to the synchronous process, such that the synchronous process is selected to determine the sales forecast faster than would the asynchronous process.
An apparatus for sales forecasting comprising a processor and memory with stored instructions. When executed, these instructions cause the processor to: identify a user and their deals needing a sales forecast; identify the data used for the forecast; identify the event type that affects the forecast. The system then manages its load by choosing either a fast (synchronous) or slow (asynchronous) forecast process based on the event type. If the event *changes deal data*, the fast process is used. If the event *changes reporting structure*, the slow process is used. Finally, the system generates the forecast using the chosen process.
Cooperative Patent Classification codes for this invention. Click any code to explore related patents in that topic.
November 8, 2012
September 3, 2013
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